最近要实现一个算法，需要用到矩阵运算，matlab的强项就是进行矩阵运算，所以要高效进行矩阵运算，就要在java中调用matlab。关于环境变量等的相关配置，请大家参考此文
http://www.cnblogs.com/allanyz/archive/2009/05/04/1449081.html。
下面我要讲的例子是matlab自带的例子，位置在MATLABroot\R2008a\toolbox\javabuilder\Examples。
大家在matlab联机帮助文件中输入matlab build for java就能搜出此例子
个人认为这个例子比较精髓，通过这个例子可以完整的弄明白java是如何调用matlab进行矩阵运算的
此例的java文件如下
代码
/* getfactor.java
* This file is used as an example for the MATLAB
* Builder JA product.
*
* Copyright 2001-2006 The MathWorks, Inc.
*/
/* Necessary package imports */
import com.mathworks.toolbox.javabuilder.*;
import factormatrix.*;
/*
* getfactor class computes cholesky, LU, and QR
* factorizations of a finite difference matrix
* of order N. The value of N is passed on the
* command line. If a second command line arg
* is passed with the value of "sparse", then
* a sparse matrix is used.
*/
class getfactor
{
public static void main(String[] args)
{
MWNumericArray a = null; /* Stores matrix to factor */
Object[] result = null; /* Stores the result */
factor theFactor = null; /* Stores factor class instance */
try
{
/* If no input, exit */
if (args.length == 0)
{
System.out.println("Error: must input a positive integer");
return;
}
/* Convert input value */
int n = Integer.valueOf(args[0]).intValue();
if (n <= 0)
{
System.out.println("Error: must input a positive integer");
return;
}
/*
* Allocate matrix. If second input is "sparse"
* allocate a sparse array
*/
int[] dims = {n, n};
if (args.length > 1 && args[1].equals("sparse"))
a = MWNumericArray.newSparse(dims[0], dims[1],n+2*(n-1), MWClassID.DOUBLE, MWComplexity.REAL);
else
a = MWNumericArray.newInstance(dims,MWClassID.DOUBLE, MWComplexity.REAL);
/* Set matrix values */
int[] index = {1, 1};
for (index[0] = 1; index[0] <= dims[0]; index[0]++)
{
for (index[1] = 1; index[1] <= dims[1]; index[1]++)
{
if (index[1] == index[0])
a.set(index, 2.0);
else if (index[1] == index[0]+1 || index[1] == index[0]-1)
a.set(index, -1.0);
}
}
/* Create new factor object */
theFactor = new factor();
/* Print original matrix */
System.out.println("Original matrix:");
System.out.println(a);
/* Compute cholesky factorization and print results. */
result = theFactor.cholesky(1, a);
System.out.println("Cholesky factorization:");
System.out.println(result[0]);
MWArray.disposeArray(result);
/* Compute LU factorization and print results. */
result = theFactor.ludecomp(2, a);
System.out.println("LU factorization:");
System.out.println("L matrix:");
System.out.println(result[0]);
System.out.println("U matrix:");
System.out.println(result[1]);
MWArray.disposeArray(result);
/* Compute QR factorization and print results. */
result = theFactor.qrdecomp(2, a);
System.out.println("QR factorization:");
System.out.println("Q matrix:");
System.out.println(result[0]);
System.out.println("R matrix:");
System.out.println(result[1]);
}
catch (Exception e)
{
System.out.println("Exception: " + e.toString());
}
finally
{
/* Free native resources */
MWArray.disposeArray(a);
MWArray.disposeArray(result);
if (theFactor != null)
theFactor.dispose();
}
}
}
* This file is used as an example for the MATLAB
* Builder JA product.
*
* Copyright 2001-2006 The MathWorks, Inc.
*/
/* Necessary package imports */
import com.mathworks.toolbox.javabuilder.*;
import factormatrix.*;
/*
* getfactor class computes cholesky, LU, and QR
* factorizations of a finite difference matrix
* of order N. The value of N is passed on the
* command line. If a second command line arg
* is passed with the value of "sparse", then
* a sparse matrix is used.
*/
class getfactor
{
public static void main(String[] args)
{
MWNumericArray a = null; /* Stores matrix to factor */
Object[] result = null; /* Stores the result */
factor theFactor = null; /* Stores factor class instance */
try
{
/* If no input, exit */
if (args.length == 0)
{
System.out.println("Error: must input a positive integer");
return;
}
/* Convert input value */
int n = Integer.valueOf(args[0]).intValue();
if (n <= 0)
{
System.out.println("Error: must input a positive integer");
return;
}
/*
* Allocate matrix. If second input is "sparse"
* allocate a sparse array
*/
int[] dims = {n, n};
if (args.length > 1 && args[1].equals("sparse"))
a = MWNumericArray.newSparse(dims[0], dims[1],n+2*(n-1), MWClassID.DOUBLE, MWComplexity.REAL);
else
a = MWNumericArray.newInstance(dims,MWClassID.DOUBLE, MWComplexity.REAL);
/* Set matrix values */
int[] index = {1, 1};
for (index[0] = 1; index[0] <= dims[0]; index[0]++)
{
for (index[1] = 1; index[1] <= dims[1]; index[1]++)
{
if (index[1] == index[0])
a.set(index, 2.0);
else if (index[1] == index[0]+1 || index[1] == index[0]-1)
a.set(index, -1.0);
}
}
/* Create new factor object */
theFactor = new factor();
/* Print original matrix */
System.out.println("Original matrix:");
System.out.println(a);
/* Compute cholesky factorization and print results. */
result = theFactor.cholesky(1, a);
System.out.println("Cholesky factorization:");
System.out.println(result[0]);
MWArray.disposeArray(result);
/* Compute LU factorization and print results. */
result = theFactor.ludecomp(2, a);
System.out.println("LU factorization:");
System.out.println("L matrix:");
System.out.println(result[0]);
System.out.println("U matrix:");
System.out.println(result[1]);
MWArray.disposeArray(result);
/* Compute QR factorization and print results. */
result = theFactor.qrdecomp(2, a);
System.out.println("QR factorization:");
System.out.println("Q matrix:");
System.out.println(result[0]);
System.out.println("R matrix:");
System.out.println(result[1]);
}
catch (Exception e)
{
System.out.println("Exception: " + e.toString());
}
finally
{
/* Free native resources */
MWArray.disposeArray(a);
MWArray.disposeArray(result);
if (theFactor != null)
theFactor.dispose();
}
}
}
